Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 10
2926-2931

Entity recommendation model based on user preference propagation of knowledge graph

Liu Qin1
Chen Shiping1
Huo Huan1,2,3
1. School of Optical-Electrical & Computer Engineering, University of Shanghai for Science & Technology, Shanghai 200093, China
2. Shanghai Key Laboratory of Data Science, Fudan University, Shanghai 201203, China
3. School of Computer Science, University of Technology Sydney, Sydney NSW2007, Australia
DOI: 10.19734/j.issn.1001-3695.2019.06.0202

Abstract

Knowledge graph is a useful tool when introducing it into the recommendation system as auxiliary information. It can effectively enhance the learning ability of the recommendation system, improving the system's accuracy and user's satisfaction. Aiming at the problem of the preference propagation on the knowledge graph, this paper proposed an entity recommendation model based on the user preference propagation of the knowledge graph. This model took the transmission intensity into consideration, while propagated the preference at the same time, thus improved the final effect of recommendation. It controlled the propagation intensity of user's preference on the knowledge graph by extracting the basic characteristics of different specific attributes, and iteratively calculated the historical preference data of each user to obtain the preference propagation model of user-item pair. Later, employing the sorting learning algorithm to get the top N recommendations. In the end, comparison experiments on three different kinds of datasets verified the effectiveness of the proposed model. This study shows that controlling the propagation intensity during the propagating process can significantly improve the accuracy rate, recall rate, as well as the F1 value of the recommendation system, and this method also has strong flexibility and interpretability.

Foundation Support

国家自然科学基金资助项目(61472256,61170277,61003031)
上海重点科技攻关项目(14511107902)
上海市工程中心建设项目(GCZX14014)
上海市一流学科建设项目(S1201YLXK,XTKX2012)
上海市数据科学重点实验室开放课题资助项目(201609060003)
沪江基金资助项目(A14006)
沪江基金研究基地专项资助项目(C14001)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2019.06.0202
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 10
Section: Algorithm Research & Explore
Pages: 2926-2931
Serial Number: 1001-3695(2020)10-009-2926-06

Publish History

[2020-10-05] Printed Article

Cite This Article

刘勤, 陈世平, 霍欢. 基于知识图谱用户偏好传播的实体推荐模型 [J]. 计算机应用研究, 2020, 37 (10): 2926-2931. (Liu Qin, Chen Shiping, Huo Huan. Entity recommendation model based on user preference propagation of knowledge graph [J]. Application Research of Computers, 2020, 37 (10): 2926-2931. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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